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Record W2911325246 · doi:10.1002/mp.13413

Cherenkov emission‐based external radiotherapy dosimetry: II. Electron beam quality specification and uncertainties

2019· article· en· W2911325246 on OpenAlexafffund
Yana Zlateva, Bryan Muir, Jan Seuntjens, Issam El Naqa

Bibliographic record

VenueMedical Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsNational Research Council CanadaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesMcGill University Health CentreNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDosimetryTruebeamLaser beam qualityBeam (structure)Monte Carlo methodPhysicsCherenkov radiationOpticsCathode rayDetectorCalibrationQuality assuranceMedical physicsElectronLinear particle acceleratorNuclear medicineNuclear physicsMathematicsEngineeringMedicineStatistics

Abstract

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Purpose Cherenkov emission (CE) is ubiquitous in external radiotherapy. It is also unique in that it carries the promise of 3D, micrometer‐resolution, perturbation‐free, in‐water dosimetry with a beam quality‐independent detector response calibration. Our aim is to bring CE‐based dosimetry into the clinic and we motivate this here with electron beams. We Monte Carlo (MC) calculate and characterize broad‐beam CE‐to‐dose conversion factors in water for a clinically representative library of electron beam qualities, address beam quality specification and reference depth selection, and develop a preliminary uncertainty budget based on our MC results and relative experimental work of a companion study (Paper I). Methods Broad electron beam CE‐to‐dose conversion factors include CE generated at polar anglesθ ± δθon beam axis in water. With modifications to the EGSnrc code SPRRZnrc, factors are calculated for a total of 20 electron beam qualities from four BEAMnrc models (Varian Clinac 2100C/D, Clinac 21EX, TrueBeam, and Elekta Precise). We examine beam quality, depth, and detection angle dependence for (4π detection), , , and . As discussed in Paper I, 4π detection offers the strongest CE‐dose correlation and with small δθis most practical. The two additional configurations are considered as a compromise between these two extremes. We address beam quality specification and reference depth selection in terms of the electron beam quality specifier , obtained from the depth of 50% CE , and derive a best‐case uncertainty budget for the CE‐based dosimetry formalism proposed in Paper I at each detection configuration. Results The factor was demonstrated to capture variations in the beam spectrum, angle, photon contamination, and electron fluence below the CE threshold (∼260 keV in the visible) in accordance with theory. The root‐mean‐square deviation and maximum deviation of a second‐order polynomial fit of simulated values in terms of were 0.05 and 0.11 mm at 4π and 0.20 and 0.33 mm at detection, respectively. The fit performance on experimental data in Paper I was in agreement with these values within experimental uncertainties (±1.5 mm, 95% CI). A two‐term power function fit of in terms of at a reference depth resulted in total ‐dependent dose uncertainty contribution estimate of 0.8% and 1.1% and preliminary best‐case estimate of the combined standard dose uncertainty of 1.1% and 1.3% at 4π and detection, respectively. The results and corresponding uncertainties with the two intermediate apertures were generally of the same order as the 4π case. In addition, a theoretically consistent downstream shift of the percent‐depth CE (PDC) by the difference between and improved the depth dependence of the 4π conversion by an order of magnitude (±2.8%). Therefore, a large aperture centered on aθvalue between and combined with a downstream PDC shift may be recommended for beam‐axis CE‐based electron beam dosimetry in water. Conclusions By delivering ‐based CE‐to‐dose conversion data and demonstrating the potential for dosimetric uncertainty on the order of 1%, we bring CE‐based electron beam dosimetry closer to clinical realization.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.313
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations17
Published2019
Admission routes2
Has abstractyes

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